AGEO PLAGAST_Clinical Final analysis 06122024
library(swimplot) library(coxphf) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(ComplexHeatmap) library(tidyverse) library(readxl) library(survival) library(janitor) library(openxlsx) library(writexl) library(rms) library(pROC) library(DT)
#ctDNA Detection rate by Stage and Window
#Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#C2D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.C2D1 %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C2D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C2D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C2D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#post-NAC Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postNAC %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postNAC == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postNAC, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postNAC == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#On-treatment
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.onACT <- factor(circ_data$ctDNA.onACT, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.onACT %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.onACT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.onACT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.onACT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Post-ACT Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postACT <- factor(circ_data$ctDNA.postACT, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postACT %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postACT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postACT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postACT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Post-relapse Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postRelapse <- factor(circ_data$ctDNA.postRelapse, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postRelapse %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postRelapse == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postRelapse, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postRelapse == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)#Demographics Table
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data_subset <- circ_data %>%
select(
Age,
Gender,
PrimSite,
NAC,
NAC.Regimen,
TRG.Mandard,
TNM,
Stage,
Grade,
Lauren.Class,
Margins,
ACT,
ACT.Regimen,
DFS.Event,
OS.Event,
FU.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
PrimSite = factor(PrimSite, levels = c("Stomach", "G/J", "Oesophagus")),
NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Therapy", "Upfront Surgery")),
NAC.Regimen = factor(NAC.Regimen),
TRG.Mandard = factor(TRG.Mandard, levels = c("TRG1","TRG2", "TRG3", "TRG4", "TRG5")),
TNM = factor(TNM, levels = c("T0-TisN0M0","T1-T2N0", "T2-T3N0-N1", "T2N1-N2", "T3N2-N3", "T4N0-N1", "T4N2-N3")),
Stage = factor(Stage, levels = c("0","I","II", "III")),
Grade = factor(Grade, levels = c("G1", "G2", "G3")),
Lauren.Class = factor(Lauren.Class),
Margins = factor(Margins, levels = c("R0", "R1")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Treatment", "Observation")),
ACT.Regimen = factor(ACT.Regimen),
DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
FU.months = as.numeric(FU.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 621 |
|---|---|
| Age | 66 (34 - 86) |
| Gender | |
| Â Â Â Â Male | 39 (63%) |
| Â Â Â Â Female | 23 (37%) |
| PrimSite | |
| Â Â Â Â Stomach | 38 (61%) |
| Â Â Â Â G/J | 24 (39%) |
| Â Â Â Â Oesophagus | 0 (0%) |
| NAC | |
| Â Â Â Â Neoadjuvant Therapy | 55 (89%) |
| Â Â Â Â Upfront Surgery | 7 (11%) |
| NAC.Regimen | |
| Â Â Â Â | 7 (11%) |
| Â Â Â Â Chemoimmunotherapy | 7 (11%) |
| Â Â Â Â Chemotherapy | 47 (76%) |
| Â Â Â Â Radiotherapy | 1 (1.6%) |
| TRG.Mandard | |
| Â Â Â Â TRG1 | 3 (5.5%) |
| Â Â Â Â TRG2 | 8 (15%) |
| Â Â Â Â TRG3 | 21 (38%) |
| Â Â Â Â TRG4 | 16 (29%) |
| Â Â Â Â TRG5 | 7 (13%) |
| Â Â Â Â Unknown | 7 |
| TNM | |
| Â Â Â Â T0-TisN0M0 | 3 (4.8%) |
| Â Â Â Â T1-T2N0 | 16 (26%) |
| Â Â Â Â T2-T3N0-N1 | 17 (27%) |
| Â Â Â Â T2N1-N2 | 2 (3.2%) |
| Â Â Â Â T3N2-N3 | 8 (13%) |
| Â Â Â Â T4N0-N1 | 9 (15%) |
| Â Â Â Â T4N2-N3 | 7 (11%) |
| Stage | |
| Â Â Â Â 0 | 3 (4.8%) |
| Â Â Â Â I | 16 (26%) |
| Â Â Â Â II | 26 (42%) |
| Â Â Â Â III | 17 (27%) |
| Grade | |
| Â Â Â Â G1 | 11 (28%) |
| Â Â Â Â G2 | 21 (53%) |
| Â Â Â Â G3 | 8 (20%) |
| Â Â Â Â Unknown | 22 |
| Lauren.Class | |
| Â Â Â Â | 5 (8.1%) |
| Â Â Â Â Diffuse | 18 (29%) |
| Â Â Â Â Intestinal | 34 (55%) |
| Â Â Â Â Mixed | 5 (8.1%) |
| Margins | |
| Â Â Â Â R0 | 61 (98%) |
| Â Â Â Â R1 | 1 (1.6%) |
| ACT | |
| Â Â Â Â Adjuvant Treatment | 53 (85%) |
| Â Â Â Â Observation | 9 (15%) |
| ACT.Regimen | |
| Â Â Â Â | 9 (15%) |
| Â Â Â Â Chemoimmunotherapy | 4 (6.5%) |
| Â Â Â Â Chemotherapy | 48 (77%) |
| Â Â Â Â Immunotherapy | 1 (1.6%) |
| DFS.Event | |
| Â Â Â Â Recurrence | 29 (47%) |
| Â Â Â Â No Recurrence | 33 (53%) |
| OS.Event | |
| Â Â Â Â Deceased | 19 (31%) |
| Â Â Â Â Alive | 43 (69%) |
| FU.months | 29 (2 - 93) |
| 1 Median (Range); n (%) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE,
strip_md_bold = TRUE)Warning: The `strip_md_bold` argument of `as_flex_table()` is deprecated as of gtsummary 1.6.0.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.fit1Characteristic | N = 621 |
|---|---|
Age | 66 (34 - 86) |
Gender | |
Male | 39 (63%) |
Female | 23 (37%) |
PrimSite | |
Stomach | 38 (61%) |
G/J | 24 (39%) |
Oesophagus | 0 (0%) |
NAC | |
Neoadjuvant Therapy | 55 (89%) |
Upfront Surgery | 7 (11%) |
NAC.Regimen | |
7 (11%) | |
Chemoimmunotherapy | 7 (11%) |
Chemotherapy | 47 (76%) |
Radiotherapy | 1 (1.6%) |
TRG.Mandard | |
TRG1 | 3 (5.5%) |
TRG2 | 8 (15%) |
TRG3 | 21 (38%) |
TRG4 | 16 (29%) |
TRG5 | 7 (13%) |
Unknown | 7 |
TNM | |
T0-TisN0M0 | 3 (4.8%) |
T1-T2N0 | 16 (26%) |
T2-T3N0-N1 | 17 (27%) |
T2N1-N2 | 2 (3.2%) |
T3N2-N3 | 8 (13%) |
T4N0-N1 | 9 (15%) |
T4N2-N3 | 7 (11%) |
Stage | |
0 | 3 (4.8%) |
I | 16 (26%) |
II | 26 (42%) |
III | 17 (27%) |
Grade | |
G1 | 11 (28%) |
G2 | 21 (53%) |
G3 | 8 (20%) |
Unknown | 22 |
Lauren.Class | |
5 (8.1%) | |
Diffuse | 18 (29%) |
Intestinal | 34 (55%) |
Mixed | 5 (8.1%) |
Margins | |
R0 | 61 (98%) |
R1 | 1 (1.6%) |
ACT | |
Adjuvant Treatment | 53 (85%) |
Observation | 9 (15%) |
ACT.Regimen | |
9 (15%) | |
Chemoimmunotherapy | 4 (6.5%) |
Chemotherapy | 48 (77%) |
Immunotherapy | 1 (1.6%) |
DFS.Event | |
Recurrence | 29 (47%) |
No Recurrence | 33 (53%) |
OS.Event | |
Deceased | 19 (31%) |
Alive | 43 (69%) |
FU.months | 29 (2 - 93) |
1Median (Range); n (%) | |
save_as_docx(fit1, path= "~/Downloads/table1.docx")#Heatmap with Clinical & Genomics Factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)
ha <- HeatmapAnnotation(
Stage = circ_data$Stage,
Gender = circ_data$Gender,
PrimSite = circ_data$PrimSite,
NAC = circ_data$NAC,
ACT = circ_data$ACT,
ctDNA.Base = circ_data$ctDNA.Base,
ctDNA.C2D1 = circ_data$ctDNA.C2D1,
ctDNA.postNAC = circ_data$ctDNA.postNAC,
ctDNA.MRD = circ_data$ctDNA.MRD,
ctDNA.surveillance = circ_data$ctDNA.surveillance,
DFS.Event = circ_data$DFS.Event,
OS.Event = circ_data$OS.Event,
col = list(Stage = c("0" = "seagreen1", "I" = "seagreen1", "II" = "orange", "III" = "purple"),
Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
PrimSite = c("Stomach" = "brown", "G/J" = "darkgreen", "Oesophagus" = "orange4"),
NAC = c("FALSE" = "cornflowerblue", "TRUE" ="darkmagenta"),
ACT = c("TRUE" = "brown4", "FALSE" ="khaki"),
ctDNA.Base = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.C2D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.postNAC = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
DFS.Event = c("TRUE" = "red3", "FALSE" ="blue"),
OS.Event = c("TRUE" = "black", "FALSE" ="gray")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()null device
1 #Overview Plot
setwd("~/Downloads")
clinstage<- read.csv("PLAGAST_OP.csv")
clinstage_df<- as.data.frame(clinstage)
#Display the swimmer plot with the label box
oplot<-swimmer_plot(df=clinstage_df,
id='PatientName',
end='fu.diff.months',
fill='gray',
width=.01,)
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(-12, 96, by = 6))
oplot <- oplot + labs(x ="Patients" , y="Months from Surgery")
oplot
##plot events
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
id='PatientName',
time='date.diff.months',
name_shape ='Event_type',
name_col = 'Event',
size=3.5,fill='black',
#col='darkgreen'
)
oplot_ev1Warning: Removed 114 rows containing missing values or values outside the scale range (`geom_point()`).
#Shape customization to Event_type
oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",values=c(1,16,6,18,4),breaks=c('ctDNA_neg','ctDNA_pos','Imaging','Surgery','Death'))
oplot_ev1.1Warning: Removed 114 rows containing missing values or values outside the scale range (`geom_point()`).
#plot treatment
oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
id='PatientName',
start='Tx_start.months',
end='Tx_end.months',
name_col='Tx_type',
size=3.5,
name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2 Warning: Removed 114 rows containing missing values or values outside the scale range (`geom_point()`).
Warning: Removed 405 rows containing missing values or values outside the scale range (`geom_segment()`).
#colour customization
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "purple","black","black", "lightblue", "green", "red", "blue","orange"))
oplot_ev2.2Warning: Removed 114 rows containing missing values or values outside the scale range (`geom_point()`).
Warning: Removed 405 rows containing missing values or values outside the scale range (`geom_segment()`).
#RFS by ctDNA at Baseline - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Base, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Base, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Base=NEGATIVE 17 5 NA 42.1 NA
ctDNA.Base=POSITIVE 39 20 22.3 13.3 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA Baseline | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Base, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Base=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 11.000 4.000 0.756 0.106 0.473 0.901
ctDNA.Base=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 13.0000 19.0000 0.4772 0.0849 0.3057 0.6301 circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Base, data = circ_data)
n= 56, number of events= 25
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.BasePOSITIVE 0.9196 2.5082 0.5030 1.828 0.0675 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.BasePOSITIVE 2.508 0.3987 0.9359 6.722
Concordance= 0.604 (se = 0.041 )
Likelihood ratio test= 3.92 on 1 df, p=0.05
Wald test = 3.34 on 1 df, p=0.07
Score (logrank) test = 3.58 on 1 df, p=0.06cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.51 (0.94-6.72); p = 0.068"#OS by ctDNA at Baseline - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Base, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Base, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Base=NEGATIVE 17 4 NA 52.7 NA
ctDNA.Base=POSITIVE 39 13 NA 24.5 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Baseline | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Base, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Base=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 14.0000 1.0000 0.9412 0.0571 0.6502 0.9915
ctDNA.Base=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 17.0000 11.0000 0.6832 0.0809 0.4963 0.8129 circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Base, data = circ_data)
n= 56, number of events= 17
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.BasePOSITIVE 0.7361 2.0878 0.5781 1.273 0.203
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.BasePOSITIVE 2.088 0.479 0.6724 6.483
Concordance= 0.597 (se = 0.05 )
Likelihood ratio test= 1.81 on 1 df, p=0.2
Wald test = 1.62 on 1 df, p=0.2
Score (logrank) test = 1.69 on 1 df, p=0.2cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.09 (0.67-6.48); p = 0.203"#RFS by ctDNA levels at Baseline based on median
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
# Calculate the quartiles of p_6mo_MTM
circ_data$ctDNA.Base.MTM <- as.numeric(as.character(circ_data$ctDNA.Base.MTM))
median_value <- median(circ_data$ctDNA.Base.MTM, na.rm = TRUE)
print(median_value)[1] 0.385# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA
circ_data <- circ_data %>%
mutate(ctDNA.6mMTM.Q = case_when(
ctDNA.Base.MTM < 0.385 ~ 1,
ctDNA.Base.MTM >= 0.385 ~ 2
))
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.6mMTM.Q, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.6mMTM.Q, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6mMTM.Q=1 28 8 NA NA NA
ctDNA.6mMTM.Q=2 28 17 14.6 9.63 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA MTM/mL groups at Baseline", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.385", "MTM/mL≥0.385"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6mMTM.Q=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 16.0000 6.0000 0.7701 0.0831 0.5559 0.8903
ctDNA.6mMTM.Q=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 8.0000 17.0000 0.3600 0.0954 0.1829 0.5409 circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.385", "MTM/mL≥0.385"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data)
n= 56, number of events= 25
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6mMTM.QMTM/mL≥0.385 1.0941 2.9865 0.4331 2.526 0.0115 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6mMTM.QMTM/mL≥0.385 2.987 0.3348 1.278 6.979
Concordance= 0.639 (se = 0.049 )
Likelihood ratio test= 6.94 on 1 df, p=0.008
Wald test = 6.38 on 1 df, p=0.01
Score (logrank) test = 7 on 1 df, p=0.008cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.99 (1.28-6.98); p = 0.012"#OS by ctDNA levels at Baseline based on median
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
# Calculate the quartiles of p_6mo_MTM
circ_data$ctDNA.Base.MTM <- as.numeric(as.character(circ_data$ctDNA.Base.MTM))
median_value <- median(circ_data$ctDNA.Base.MTM, na.rm = TRUE)
print(median_value)[1] 0.385# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA
circ_data <- circ_data %>%
mutate(ctDNA.6mMTM.Q = case_when(
ctDNA.Base.MTM < 0.385 ~ 1,
ctDNA.Base.MTM >= 0.385 ~ 2
))
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.6mMTM.Q, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.6mMTM.Q, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6mMTM.Q=1 28 6 NA NA NA
ctDNA.6mMTM.Q=2 28 11 NA 23.9 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MTM/mL groups at Baseline", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.385", "MTM/mL≥0.385"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6mMTM.Q=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 19.0000 3.0000 0.8914 0.0592 0.6999 0.9637
ctDNA.6mMTM.Q=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 12.0000 9.0000 0.6444 0.0976 0.4216 0.7997 circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.385", "MTM/mL≥0.385"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data)
n= 56, number of events= 17
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6mMTM.QMTM/mL≥0.385 0.8713 2.3901 0.5143 1.694 0.0902 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6mMTM.QMTM/mL≥0.385 2.39 0.4184 0.8722 6.549
Concordance= 0.609 (se = 0.062 )
Likelihood ratio test= 3.03 on 1 df, p=0.08
Wald test = 2.87 on 1 df, p=0.09
Score (logrank) test = 3.04 on 1 df, p=0.08cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.39 (0.87-6.55); p = 0.09"#RFS by ctDNA levels at Baseline based on AUC optimal MTM/ml level
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
#DFS.Event
circ_data <- circ_data[complete.cases(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM),]
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
ROC <- roc(DFS.Event ~ ctDNA.Base.MTM, data = circ_data, ci = TRUE)Setting levels: control = FALSE, case = TRUE
Setting direction: controls < casesp<-ggroc(ROC,
aes = c("linetype"), color = "blue", size = 1,
legacy.axes = TRUE) +
geom_abline(color = "dark grey", size = 0.5) +
theme_classic()+
ylab("Sensitivity") + theme(axis.title.x = element_text(color="black", size=14), axis.title.y = element_text(color="black", size=14),axis.text.x = element_text(colour = "black", size=14),axis.text.y = element_text(colour = "black",size=14),legend.title = element_blank(),legend.text = element_text(size=14))Warning in ggplot2::geom_line(aes$aes, ...) :
Ignoring unknown parameters: `aes`
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.p
#AUC
AUC <- auc(ROC)
print(AUC)Area under the curve: 0.6529AUC_conf <- ci.auc(ROC)
print(AUC_conf)95% CI: 0.5062-0.7996 (DeLong)res.roc <- roc(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM)Setting levels: control = FALSE, case = TRUE
Setting direction: controls < casesplot.roc(res.roc, print.auc = TRUE, print.thres = "best")
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.6mMTM.Q = case_when(
ctDNA.Base.MTM < 0.845 ~ 1,
ctDNA.Base.MTM >= 0.845 ~ 2
))
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.6mMTM.Q, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.6mMTM.Q, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6mMTM.Q=1 31 9 NA NA NA
ctDNA.6mMTM.Q=2 25 16 14.5 7.79 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA MTM/mL groups at Baseline", ylab= "Disease-Free Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.845", "MTM/mL≥0.845"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6mMTM.Q=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 18.0000 7.0000 0.7544 0.0814 0.5504 0.8755
ctDNA.6mMTM.Q=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 6.0000 16.0000 0.3267 0.0989 0.1494 0.5178 circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.845", "MTM/mL≥0.845"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data)
n= 56, number of events= 25
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6mMTM.QMTM/mL≥0.845 1.2620 3.5325 0.4254 2.966 0.00301 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6mMTM.QMTM/mL≥0.845 3.533 0.2831 1.534 8.132
Concordance= 0.662 (se = 0.048 )
Likelihood ratio test= 9.29 on 1 df, p=0.002
Wald test = 8.8 on 1 df, p=0.003
Score (logrank) test = 9.89 on 1 df, p=0.002cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 3.53 (1.53-8.13); p = 0.003"#OS by ctDNA levels at Baseline based on AUC optimal MTM/mL level
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
#OS.Event
circ_data <- circ_data[complete.cases(circ_data$OS.Event, circ_data$ctDNA.Base.MTM),]
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
ROC <- roc(OS.Event ~ ctDNA.Base.MTM, data = circ_data, ci = TRUE)Setting levels: control = FALSE, case = TRUE
Setting direction: controls < casesp<-ggroc(ROC,
aes = c("linetype"), color = "blue", size = 1,
legacy.axes = TRUE) +
geom_abline(color = "dark grey", size = 0.5) +
theme_classic()+
ylab("Sensitivity") + theme(axis.title.x = element_text(color="black", size=14), axis.title.y = element_text(color="black", size=14),axis.text.x = element_text(colour = "black", size=14),axis.text.y = element_text(colour = "black",size=14),legend.title = element_blank(),legend.text = element_text(size=14))Warning in ggplot2::geom_line(aes$aes, ...) :
Ignoring unknown parameters: `aes`p
#AUC
AUC <- auc(ROC)
print(AUC)Area under the curve: 0.6463AUC_conf <- ci.auc(ROC)
print(AUC_conf)95% CI: 0.4742-0.8184 (DeLong)res.roc <- roc(circ_data$OS.Event, circ_data$ctDNA.Base.MTM)Setting levels: control = FALSE, case = TRUE
Setting direction: controls < casesplot.roc(res.roc, print.auc = TRUE, print.thres = "best")
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.6mMTM.Q = case_when(
ctDNA.Base.MTM < 11.305 ~ 1,
ctDNA.Base.MTM >= 11.305 ~ 2
))
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.6mMTM.Q, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.6mMTM.Q, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6mMTM.Q=1 44 9 NA NA NA
ctDNA.6mMTM.Q=2 12 8 19.5 13.3 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MTM/mL groups at Baseline", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<11.305", "MTM/mL≥11.305"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6mMTM.Q=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 25.0000 6.0000 0.8447 0.0594 0.6826 0.9281
ctDNA.6mMTM.Q=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 6.000 0.500 0.144 0.208 0.736 circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<11.305", "MTM/mL≥11.305"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data)
n= 56, number of events= 17
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6mMTM.QMTM/mL≥11.305 1.4951 4.4599 0.4981 3.002 0.00268 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6mMTM.QMTM/mL≥11.305 4.46 0.2242 1.68 11.84
Concordance= 0.66 (se = 0.06 )
Likelihood ratio test= 8.21 on 1 df, p=0.004
Wald test = 9.01 on 1 df, p=0.003
Score (logrank) test = 10.71 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.46 (1.68-11.84); p = 0.003"#OS by ctDNA levels at Baseline based on AUC optimal MTM/mL level from RFS model
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.6mMTM.Q = case_when(
ctDNA.Base.MTM < 0.845 ~ 1,
ctDNA.Base.MTM >= 0.845 ~ 2
))
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.6mMTM.Q, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.6mMTM.Q, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6mMTM.Q=1 31 6 NA NA NA
ctDNA.6mMTM.Q=2 25 11 38 18.9 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MTM/mL groups at Baseline", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.845", "MTM/mL≥0.845"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6mMTM.Q=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 22.0000 3.0000 0.9021 0.0537 0.7262 0.9673
ctDNA.6mMTM.Q=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 9.000 9.000 0.593 0.109 0.354 0.769 circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.845", "MTM/mL≥0.845"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data)
n= 56, number of events= 17
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6mMTM.QMTM/mL≥0.845 1.1983 3.3146 0.5185 2.311 0.0208 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6mMTM.QMTM/mL≥0.845 3.315 0.3017 1.2 9.158
Concordance= 0.644 (se = 0.062 )
Likelihood ratio test= 5.68 on 1 df, p=0.02
Wald test = 5.34 on 1 df, p=0.02
Score (logrank) test = 5.93 on 1 df, p=0.01cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 3.31 (1.2-9.16); p = 0.021"#RFS by ctDNA on-NAT - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.C2D1!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.C2D1, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.C2D1, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C2D1=NEGATIVE 20 4 NA NA NA
ctDNA.C2D1=POSITIVE 21 14 13.3 6.57 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA status on-NAT | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.C2D1, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C2D1=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 14.0000 3.0000 0.8400 0.0853 0.5792 0.9459
ctDNA.C2D1=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 4.000 13.000 0.342 0.109 0.146 0.550 circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C2D1, data = circ_data)
n= 41, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C2D1POSITIVE 1.8205 6.1750 0.5767 3.157 0.0016 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C2D1POSITIVE 6.175 0.1619 1.994 19.12
Concordance= 0.718 (se = 0.046 )
Likelihood ratio test= 12.42 on 1 df, p=4e-04
Wald test = 9.97 on 1 df, p=0.002
Score (logrank) test = 12.64 on 1 df, p=4e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.17 (1.99-19.12); p = 0.002"#OS by ctDNA on-NAT - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.C2D1!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.C2D1, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.C2D1, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C2D1=NEGATIVE 20 3 NA NA NA
ctDNA.C2D1=POSITIVE 21 9 24.5 14.4 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA status on-NAT | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.C2D1, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C2D1=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 15.0000 1.0000 0.9500 0.0487 0.6947 0.9928
ctDNA.C2D1=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 8.000 0.586 0.114 0.335 0.771 circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C2D1, data = circ_data)
n= 41, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C2D1POSITIVE 1.5506 4.7145 0.6795 2.282 0.0225 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C2D1POSITIVE 4.714 0.2121 1.245 17.86
Concordance= 0.706 (se = 0.054 )
Likelihood ratio test= 6.18 on 1 df, p=0.01
Wald test = 5.21 on 1 df, p=0.02
Score (logrank) test = 6.2 on 1 df, p=0.01cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.71 (1.24-17.86); p = 0.022"#DFS by ctDNA Clearance during NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.C2D1.Clearance <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.C2D1.Clearance = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" ~ "TRUE",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" ~ "FALSE",
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.C2D1.Clearance),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.C2D1.Clearance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.C2D1.Clearance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C2D1.Clearance=FALSE 20 13 13.3 7.36 NA
ctDNA.C2D1.Clearance=TRUE 9 2 NA 18.20 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("red","blue"), title="RFS - ctDNA clearance C2D1", ylab= "Recurrence-Free Survival", xlab="Months from Surgery", legend.labs=c("No Clearance", "Clearance"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C2D1.Clearance=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 4.000 12.000 0.359 0.114 0.153 0.572
ctDNA.C2D1.Clearance=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 5.000 2.000 0.741 0.161 0.289 0.930 circ_data$ctDNA.C2D1.Clearance <- factor(circ_data$ctDNA.C2D1.Clearance, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1.Clearance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C2D1.Clearance, data = circ_data)
n= 29, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C2D1.ClearanceFALSE 1.6403 5.1567 0.7649 2.144 0.032 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C2D1.ClearanceFALSE 5.157 0.1939 1.152 23.09
Concordance= 0.667 (se = 0.046 )
Likelihood ratio test= 6.5 on 1 df, p=0.01
Wald test = 4.6 on 1 df, p=0.03
Score (logrank) test = 5.66 on 1 df, p=0.02cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.16 (1.15-23.09); p = 0.032"#OS by ctDNA Clearance during NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.C2D1.Clearance <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.C2D1.Clearance = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" ~ "TRUE",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" ~ "FALSE",
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.C2D1.Clearance),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.C2D1.Clearance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.C2D1.Clearance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C2D1.Clearance=FALSE 20 8 NA 18.9 NA
ctDNA.C2D1.Clearance=TRUE 9 1 NA 38.0 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("red","blue"), title="OS - ctDNA clearance C2D1", ylab= "Overall Survival", xlab="Months from Surgery", legend.labs=c("No Clearance", "Clearance"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C2D1.Clearance=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 7.000 0.616 0.116 0.353 0.798
ctDNA.C2D1.Clearance=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 6 0 1 0 NA NA circ_data$ctDNA.C2D1.Clearance <- factor(circ_data$ctDNA.C2D1.Clearance, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1.Clearance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C2D1.Clearance, data = circ_data)
n= 29, number of events= 9
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C2D1.ClearanceFALSE 1.634 5.126 1.064 1.536 0.125
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C2D1.ClearanceFALSE 5.126 0.1951 0.6369 41.25
Concordance= 0.669 (se = 0.047 )
Likelihood ratio test= 3.48 on 1 df, p=0.06
Wald test = 2.36 on 1 df, p=0.1
Score (logrank) test = 2.92 on 1 df, p=0.09cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.13 (0.64-41.25); p = 0.125"#RFS by ctDNA post-NAT - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.postNAC, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.postNAC, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.postNAC=NEGATIVE 30 10 NA 21.52 NA
ctDNA.postNAC=POSITIVE 11 8 7.79 3.55 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postNAC, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA status post-NAT | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.postNAC, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.postNAC=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 13.0000 10.0000 0.6377 0.0933 0.4269 0.7883
ctDNA.postNAC=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 8.00000 0.13636 0.12392 0.00767 0.44263 circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postNAC, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.postNAC, data = circ_data)
n= 41, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.postNACPOSITIVE 1.6603 5.2610 0.5036 3.297 0.000978 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.postNACPOSITIVE 5.261 0.1901 1.961 14.12
Concordance= 0.669 (se = 0.052 )
Likelihood ratio test= 9.69 on 1 df, p=0.002
Wald test = 10.87 on 1 df, p=0.001
Score (logrank) test = 13.08 on 1 df, p=3e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.26 (1.96-14.12); p = 0.001"#OS by ctDNA post-NAT - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.postNAC, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.postNAC, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.postNAC=NEGATIVE 30 5 NA NA NA
ctDNA.postNAC=POSITIVE 11 8 18.9 11.7 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postNAC, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA status post-NAT | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.postNAC, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.postNAC=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 16.0000 3.0000 0.9000 0.0548 0.7212 0.9666
ctDNA.postNAC=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 7.0000 0.2273 0.1409 0.0346 0.5207 circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postNAC, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.postNAC, data = circ_data)
n= 41, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.postNACPOSITIVE 1.9944 7.3479 0.5811 3.432 0.000599 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.postNACPOSITIVE 7.348 0.1361 2.352 22.95
Concordance= 0.711 (se = 0.07 )
Likelihood ratio test= 11.82 on 1 df, p=6e-04
Wald test = 11.78 on 1 df, p=6e-04
Score (logrank) test = 15.82 on 1 df, p=7e-05cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.35 (2.35-22.95); p = 0.001"#Multivariate cox regression for RFS - ctDNA post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.postNAC + PrimSite + Gender + Age.Group + ACT, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for RFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#Multivariate cox regression for OS - ctDNA post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.postNAC + PrimSite + Gender + Age.Group + ACT, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#RFS by ctDNA post-NAT & ypT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.postNAC == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.postNAC == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 25 6 NA NA NA
ctDNA.Stage.II.TNM=2 8 5 9.63 6.21 NA
ctDNA.Stage.II.TNM=3 5 4 7.36 2.53 NA
ctDNA.Stage.II.TNM=4 3 3 3.55 1.35 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA post-NAT & ypTN", ylab= "Disease-Free Survival", xlab="Time from Surgery (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 25 0 1.000 0.0000 1.000 1.000
24 13 6 0.733 0.0944 0.497 0.872
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1.000 0.000 1.00 1.000
24 1 5 0.208 0.179 0.01 0.586
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 41, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 1.9291 6.8836 0.6303 3.061 0.00221 **
ctDNA.Stage.II.TNMctDNA(-) & T4 2.2440 9.4313 0.6932 3.237 0.00121 **
ctDNA.Stage.II.TNMctDNA(+) & T4 3.2728 26.3857 0.8116 4.032 5.52e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 6.884 0.1453 2.001 23.68
ctDNA.Stage.II.TNMctDNA(-) & T4 9.431 0.1060 2.424 36.70
ctDNA.Stage.II.TNMctDNA(+) & T4 26.386 0.0379 5.376 129.49
Concordance= 0.775 (se = 0.043 )
Likelihood ratio test= 20.62 on 3 df, p=1e-04
Wald test = 19.79 on 3 df, p=2e-04
Score (logrank) test = 29.52 on 3 df, p=2e-06#Repeat analysis to compare ctDNA post-NAT (+) T1-T3 vs T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.postNAC == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.postNAC == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 25 6 NA NA NA
ctDNA.Stage.II.TNM=2 8 5 9.63 6.21 NA
ctDNA.Stage.II.TNM=3 5 4 7.36 2.53 NA
ctDNA.Stage.II.TNM=4 3 3 3.55 1.35 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 41, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNM4 1.3437 3.8331 0.7683 1.749 0.08031 .
ctDNA.Stage.II.TNM1 -1.9291 0.1453 0.6303 -3.061 0.00221 **
ctDNA.Stage.II.TNM3 0.3149 1.3701 0.6866 0.459 0.64652
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNM4 3.8331 0.2609 0.85028 17.2799
ctDNA.Stage.II.TNM1 0.1453 6.8836 0.04224 0.4997
ctDNA.Stage.II.TNM3 1.3701 0.7299 0.35669 5.2629
Concordance= 0.775 (se = 0.043 )
Likelihood ratio test= 20.62 on 3 df, p=1e-04
Wald test = 19.79 on 3 df, p=2e-04
Score (logrank) test = 29.52 on 3 df, p=2e-06#OS by ctDNA post-NAT & ypT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.postNAC == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.postNAC == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 25 2 NA NA NA
ctDNA.Stage.II.TNM=2 8 5 16.66 11.66 NA
ctDNA.Stage.II.TNM=3 5 3 8.31 3.58 NA
ctDNA.Stage.II.TNM=4 3 3 19.09 2.40 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA post-NAT & ypTN", ylab= "Overall Survival", xlab="Time from Surgery (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 25 0 1 0 1 1
24 16 0 1 0 NA NA
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1.000 0.000 1.0000 1.000
24 2 4 0.333 0.192 0.0461 0.676
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 41, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 2.7833 16.1727 0.8451 3.293 0.000990 ***
ctDNA.Stage.II.TNMctDNA(-) & T4 3.6483 38.4083 1.0196 3.578 0.000346 ***
ctDNA.Stage.II.TNMctDNA(+) & T4 3.6058 36.8115 0.9977 3.614 0.000302 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 16.17 0.06183 3.086 84.75
ctDNA.Stage.II.TNMctDNA(-) & T4 38.41 0.02604 5.207 283.33
ctDNA.Stage.II.TNMctDNA(+) & T4 36.81 0.02717 5.208 260.17
Concordance= 0.863 (se = 0.035 )
Likelihood ratio test= 24.39 on 3 df, p=2e-05
Wald test = 16.23 on 3 df, p=0.001
Score (logrank) test = 30.45 on 3 df, p=1e-06#Repeat analysis to compare ctDNA post-NAT (+) T1-T3 vs T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.postNAC == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.postNAC == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 25 2 NA NA NA
ctDNA.Stage.II.TNM=2 8 5 16.66 11.66 NA
ctDNA.Stage.II.TNM=3 5 3 8.31 3.58 NA
ctDNA.Stage.II.TNM=4 3 3 19.09 2.40 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 41, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNM4 0.82248 2.27615 0.79900 1.029 0.30329
ctDNA.Stage.II.TNM1 -2.78333 0.06183 0.84513 -3.293 0.00099 ***
ctDNA.Stage.II.TNM3 0.86495 2.37489 0.80811 1.070 0.28447
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNM4 2.27615 0.4393 0.4754 10.897
ctDNA.Stage.II.TNM1 0.06183 16.1727 0.0118 0.324
ctDNA.Stage.II.TNM3 2.37489 0.4211 0.4873 11.575
Concordance= 0.863 (se = 0.035 )
Likelihood ratio test= 24.39 on 3 df, p=2e-05
Wald test = 16.23 on 3 df, p=0.001
Score (logrank) test = 30.45 on 3 df, p=1e-06#RFS by ctDNA post-NAT & ypN
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N0" ~ 1,
ctDNA.postNAC == "POSITIVE" & ypNStatus == "N0" ~ 2,
ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
ctDNA.postNAC == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 16 2 NA NA NA
ctDNA.Stage.II.TNM=2 3 1 NA 3.55 NA
ctDNA.Stage.II.TNM=3 14 8 18.2 11.40 NA
ctDNA.Stage.II.TNM=4 8 7 7.0 2.56 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA post-NAT & ypN", ylab= "Disease-Free Survival", xlab="Time from Surgery (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 16 0 1.000 0.0000 1.000 1.000
24 12 2 0.875 0.0827 0.586 0.967
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1.000 0.000 1.0000 1.000
24 1 1 0.667 0.272 0.0541 0.945
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 14 0 1.000 0.00 1.0000 1.000
24 1 8 0.188 0.16 0.0107 0.539
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 41, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & ypN0 1.8009 6.0550 1.2294 1.465 0.14295
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 2.3395 10.3756 0.8452 2.768 0.00564 **
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 3.7725 43.4875 0.9323 4.046 5.2e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & ypN0 6.055 0.16515 0.5441 67.38
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 10.376 0.09638 1.9794 54.39
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 43.488 0.02300 6.9948 270.37
Concordance= 0.773 (se = 0.058 )
Likelihood ratio test= 23.01 on 3 df, p=4e-05
Wald test = 17.09 on 3 df, p=7e-04
Score (logrank) test = 26.38 on 3 df, p=8e-06#Repeat analysis to compare ctDNA post-NAT (-) vs (+) in ypN+
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N0" ~ 1,
ctDNA.postNAC == "POSITIVE" & ypNStatus == "N0" ~ 2,
ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
ctDNA.postNAC == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 16 2 NA NA NA
ctDNA.Stage.II.TNM=2 3 1 NA 3.55 NA
ctDNA.Stage.II.TNM=3 14 8 18.2 11.40 NA
ctDNA.Stage.II.TNM=4 8 7 7.0 2.56 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 41, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNM4 1.43302 4.19133 0.57130 2.508 0.01213 *
ctDNA.Stage.II.TNM1 -2.33945 0.09638 0.84525 -2.768 0.00564 **
ctDNA.Stage.II.TNM2 -0.53857 0.58358 1.08425 -0.497 0.61939
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNM4 4.19133 0.2386 1.36791 12.8424
ctDNA.Stage.II.TNM1 0.09638 10.3756 0.01839 0.5052
ctDNA.Stage.II.TNM2 0.58358 1.7136 0.06969 4.8867
Concordance= 0.773 (se = 0.058 )
Likelihood ratio test= 23.01 on 3 df, p=4e-05
Wald test = 17.09 on 3 df, p=7e-04
Score (logrank) test = 26.38 on 3 df, p=8e-06#OS by ctDNA post-NAT & ypN
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N0" ~ 1,
ctDNA.postNAC == "POSITIVE" & ypNStatus == "N0" ~ 2,
ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
ctDNA.postNAC == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 16 2 NA NA NA
ctDNA.Stage.II.TNM=2 3 1 19.1 19.09 NA
ctDNA.Stage.II.TNM=3 14 3 NA 24.25 NA
ctDNA.Stage.II.TNM=4 8 7 14.4 8.54 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA post-NAT & ypN", ylab= "Overall Survival", xlab="Time from Surgery (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 16 0 1.000 0.0000 1.000 1.000
24 13 1 0.938 0.0605 0.632 0.991
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1.0 0.000 1.00000 1.00
24 1 1 0.5 0.354 0.00598 0.91
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 14 0 1.000 0.0000 1.000 1.000
24 3 2 0.857 0.0935 0.539 0.962
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1.000 0.000 1.00000 1.000
24 1 6 0.146 0.135 0.00726 0.471circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 41, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & ypN0 1.4824 4.4036 1.2274 1.208 0.227138
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 1.4115 4.1022 0.9495 1.487 0.137123
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 3.1167 22.5721 0.8727 3.571 0.000355 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & ypN0 4.404 0.2271 0.3972 48.82
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 4.102 0.2438 0.6380 26.38
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 22.572 0.0443 4.0804 124.87
Concordance= 0.765 (se = 0.076 )
Likelihood ratio test= 16.79 on 3 df, p=8e-04
Wald test = 15.08 on 3 df, p=0.002
Score (logrank) test = 23.63 on 3 df, p=3e-05#Repeat analysis to compare ctDNA post-NAT (+) ypN0 vs ypN+
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N0" ~ 1,
ctDNA.postNAC == "POSITIVE" & ypNStatus == "N0" ~ 2,
ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
ctDNA.postNAC == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 41, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNM4 1.63430 5.12585 1.11292 1.468 0.142
ctDNA.Stage.II.TNM1 -1.48242 0.22709 1.22741 -1.208 0.227
ctDNA.Stage.II.TNM3 -0.07091 0.93155 1.17530 -0.060 0.952
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNM4 5.1259 0.1951 0.57869 45.403
ctDNA.Stage.II.TNM1 0.2271 4.4036 0.02048 2.517
ctDNA.Stage.II.TNM3 0.9315 1.0735 0.09306 9.325
Concordance= 0.765 (se = 0.076 )
Likelihood ratio test= 16.79 on 3 df, p=8e-04
Wald test = 15.08 on 3 df, p=0.002
Score (logrank) test = 23.63 on 3 df, p=3e-05#DFS by ctDNA Clearance post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.C2D1.Clearance <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.C2D1.Clearance = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "TRUE",
ctDNA.Base == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "FALSE",
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.C2D1.Clearance),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.C2D1.Clearance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.C2D1.Clearance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C2D1.Clearance=FALSE 10 7 7.79 2.56 NA
ctDNA.C2D1.Clearance=TRUE 17 6 NA 18.20 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("red","blue"), title="RFS - ctDNA clearance post-NAT", ylab= "Recurrence-Free Survival", xlab="Months from Surgery", legend.labs=c("No Clearance", "Clearance"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C2D1.Clearance=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 7.00000 0.15000 0.13555 0.00802 0.47435
ctDNA.C2D1.Clearance=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 6.000 0.613 0.128 0.324 0.809 circ_data$ctDNA.C2D1.Clearance <- factor(circ_data$ctDNA.C2D1.Clearance, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1.Clearance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C2D1.Clearance, data = circ_data)
n= 27, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C2D1.ClearanceFALSE 1.4237 4.1523 0.5823 2.445 0.0145 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C2D1.ClearanceFALSE 4.152 0.2408 1.326 13
Concordance= 0.668 (se = 0.064 )
Likelihood ratio test= 5.82 on 1 df, p=0.02
Wald test = 5.98 on 1 df, p=0.01
Score (logrank) test = 6.85 on 1 df, p=0.009cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.15 (1.33-13); p = 0.014"#OS by ctDNA Clearance post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.C2D1.Clearance <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.C2D1.Clearance = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "TRUE",
ctDNA.Base == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "FALSE",
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.C2D1.Clearance),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.C2D1.Clearance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.C2D1.Clearance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C2D1.Clearance=FALSE 10 7 18.9 11.7 NA
ctDNA.C2D1.Clearance=TRUE 17 2 NA NA NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("red","blue"), title="OS - ctDNA clearance post-NAT", ylab= "Overall Survival", xlab="Months from Surgery", legend.labs=c("No Clearance", "Clearance"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C2D1.Clearance=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 6.0000 0.2571 0.1560 0.0384 0.5679
ctDNA.C2D1.Clearance=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 6.0000 2.0000 0.8824 0.0781 0.6060 0.9692 circ_data$ctDNA.C2D1.Clearance <- factor(circ_data$ctDNA.C2D1.Clearance, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1.Clearance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C2D1.Clearance, data = circ_data)
n= 27, number of events= 9
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C2D1.ClearanceFALSE 2.0846 8.0411 0.8039 2.593 0.00951 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C2D1.ClearanceFALSE 8.041 0.1244 1.664 38.87
Concordance= 0.709 (se = 0.089 )
Likelihood ratio test= 8.55 on 1 df, p=0.003
Wald test = 6.72 on 1 df, p=0.01
Score (logrank) test = 9.48 on 1 df, p=0.002cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 8.04 (1.66-38.87); p = 0.01"#Association of ctDNA Dynamics post-NAT and Response
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
#Vertical Fisher plot for ctDNA clearance post-NAT and Rec Status
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
40 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=ctDNA +/-/- 5 1 NA 18.20 NA
ctDNA.Dynamics=ctDNA +/+/- 10 5 14.55 7.36 NA
ctDNA.Dynamics=ctDNA +/+/+ 7 6 6.21 2.04 NAcirc_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-", "ctDNA +/+/-", "ctDNA +/+/+"))
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$TRG)
fisher_exact_test <- fisher.test(contingency_table)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 11.378, df = 2, p-value = 0.003383print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.002557
alternative hypothesis: two.sidedprint(contingency_table)
TRG1/2/3 TRG4/5
ctDNA +/-/- 4 0
ctDNA +/+/- 6 4
ctDNA +/+/+ 0 7table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
# Swapping x and y in ggplot function to make bar plot vertical
ggplot(table_df, aes(y = Var1, x = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(x = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA Dymamics post-NAT", y = "ctDNA", x = "Patients (%)", fill = "Path Response") +
scale_x_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("TRG1/2/3" = "lightblue3", "TRG4/5" = "red")) + # define custom colors
theme(axis.text.y = element_text(angle = 0, hjust = 1.5, size = 14), # increase y-axis text size
axis.text.x = element_text(size = 14, color = "black"), # increase x-axis text size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculating p-value with Fisher exact test for the ctDNA +/+/+ vs ctDNA +/+/-
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
#Vertical Fisher plot for ctDNA clearance post-NAT and Rec Status
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="ctDNA +/-/-",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
40 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=ctDNA +/+/- 10 5 14.55 7.36 NA
ctDNA.Dynamics=ctDNA +/+/+ 7 6 6.21 2.04 NAcirc_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/+/-", "ctDNA +/+/+"))
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$TRG)
fisher_exact_test <- fisher.test(contingency_table)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 4.1295, df = 1, p-value = 0.04214print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0345
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.176016 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
TRG1/2/3 TRG4/5
ctDNA +/+/- 6 4
ctDNA +/+/+ 0 7#DFS by ctDNA Dynamics post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=ctDNA +/-/- 5 1 NA 18.20 NA
ctDNA.Dynamics=ctDNA +/+/- 10 5 14.55 7.36 NA
ctDNA.Dynamics=ctDNA +/+/+ 7 6 6.21 2.04 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA Dynamics post-NAT", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=ctDNA +/-/-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1.000 0.000 1.0000 1.000
24 2 1 0.667 0.272 0.0541 0.945
ctDNA.Dynamics=ctDNA +/+/-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.00 0.000 1.000 1.000
24 2 5 0.48 0.164 0.161 0.745
ctDNA.Dynamics=ctDNA +/+/+
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1 0 1 1 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 22, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsctDNA +/+/- 1.343 3.831 1.100 1.221 0.2219
ctDNA.DynamicsctDNA +/+/+ 2.921 18.568 1.157 2.526 0.0116 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsctDNA +/+/- 3.831 0.26100 0.4439 33.07
ctDNA.DynamicsctDNA +/+/+ 18.568 0.05386 1.9237 179.22
Concordance= 0.756 (se = 0.053 )
Likelihood ratio test= 10.01 on 2 df, p=0.007
Wald test = 8.49 on 2 df, p=0.01
Score (logrank) test = 11.47 on 2 df, p=0.003#OS by ctDNA Dynamics post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=ctDNA +/-/- 5 0 NA NA NA
ctDNA.Dynamics=ctDNA +/+/- 10 2 NA NA NA
ctDNA.Dynamics=ctDNA +/+/+ 7 6 14.4 8.54 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA Dynamics post-NAT", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=ctDNA +/-/-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1
24 2 0 1 0 NA NA
ctDNA.Dynamics=ctDNA +/+/-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.0 0.000 1.000 1.000
24 2 2 0.8 0.126 0.409 0.946
ctDNA.Dynamics=ctDNA +/+/+
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1.000 0.000 1.00000 1.000
24 1 5 0.171 0.156 0.00794 0.526circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
cox_fit <- coxphf(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.DynamicsctDNA +/+/- 1.178268 1.696304 3.248742 0.2627946 448.914 0.7281482 0.393484095
ctDNA.DynamicsctDNA +/+/+ 2.776185 1.609915 16.057642 1.8668569 2102.174 7.2774206 0.006982673
Likelihood ratio test=8.847995 on 2 df, p=0.01198622, n=22
Wald test = 5.979249 on 2 df, p = 0.05030632
Covariance-Matrix:
ctDNA.DynamicsctDNA +/+/- ctDNA.DynamicsctDNA +/+/+
ctDNA.DynamicsctDNA +/+/- 2.877449 2.403504
ctDNA.DynamicsctDNA +/+/+ 2.403504 2.591825#Levels of Baseline MTM/mL in ctDNA Dynamics post-NAT categories
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
# Transform ctDNA.Base.MTM with log10
circ_data <- subset(circ_data, !is.na(ctDNA.Dynamics))
circ_data$ctDNA.Base.MTM <- as.numeric(as.character(circ_data$ctDNA.Base.MTM))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
median_Base_MTM <- aggregate(ctDNA.Base.MTM ~ ctDNA.Dynamics, data = circ_data, FUN = median)
print(median_Base_MTM)
# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=ctDNA.Dynamics, y=ctDNA.Base.MTM, fill=ctDNA.Dynamics)) +
geom_violin(trim=FALSE) +
scale_fill_manual(values=c("ctDNA +/-/-"="lightblue", "ctDNA +/+/-"="lightgreen", "ctDNA +/+/+"="salmon")) +
geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
labs(title="Baseline MTM/mL | Dynamics post-NAT", x="Dynamics post-NAT", y="Baseline MTM/mL") +
theme_minimal() +
theme(legend.position="none")
m3_1v2 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/-/-", "ctDNA +/+/-"), ],
na.rm = TRUE)Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...) :
cannot compute exact p-value with tiesprint(m3_1v2)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by ctDNA.Dynamics
W = 26, p-value = 0.9511
alternative hypothesis: true location shift is not equal to 0m3_1v3 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/-/-", "ctDNA +/+/+"), ],
na.rm = TRUE)
print(m3_1v3)
Wilcoxon rank sum exact test
data: ctDNA.Base.MTM by ctDNA.Dynamics
W = 7, p-value = 0.1061
alternative hypothesis: true location shift is not equal to 0m3_2v3 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/+/-", "ctDNA +/+/+"), ],
na.rm = TRUE)Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...) :
cannot compute exact p-value with tiesprint(m3_2v3)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by ctDNA.Dynamics
W = 2, p-value = 0.001506
alternative hypothesis: true location shift is not equal to 0#DFS by ctDNA post-NAT & TRG combination
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.pCR <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.postNAC == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
ctDNA.postNAC == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
ctDNA.postNAC == "POSITIVE" & TRG == "TRG4/5" ~ "3"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.pCR, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.pCR=1 23 5 NA NA NA
ctDNA.pCR=2 6 5 11 2.53 NA
ctDNA.pCR=3 10 8 7 2.56 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA post-NAT/TRG", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.pCR, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.pCR=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 23 0 1.000 0.000 1.000 1.000
24 13 5 0.767 0.092 0.526 0.897
ctDNA.pCR=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1 0 1 1
ctDNA.pCR=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.000 0.000 1.00000 1.000
24 1 8 0.125 0.115 0.00702 0.418circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.pCR, data = circ_data)
n= 39, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.pCRTRG4/5 ctDNA (-) 2.1228 8.3545 0.6529 3.251 0.00115 **
ctDNA.pCRTRG4/5 ctDNA (+) 2.3546 10.5336 0.5969 3.945 7.99e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.pCRTRG4/5 ctDNA (-) 8.355 0.11970 2.323 30.04
ctDNA.pCRTRG4/5 ctDNA (+) 10.534 0.09493 3.270 33.94
Concordance= 0.765 (se = 0.044 )
Likelihood ratio test= 19.53 on 2 df, p=6e-05
Wald test = 17.01 on 2 df, p=2e-04
Score (logrank) test = 23.41 on 2 df, p=8e-06#OS by ctDNA post-NAT & TRG combination
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.pCR <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.postNAC == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
ctDNA.postNAC == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
ctDNA.postNAC == "POSITIVE" & TRG == "TRG4/5" ~ "3"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.pCR, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.pCR=1 23 2 NA NA NA
ctDNA.pCR=2 6 3 8.31 3.58 NA
ctDNA.pCR=3 10 8 18.89 11.66 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA post-NAT/TRG", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.pCR, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.pCR=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 23 0 1 0 1 1
24 15 0 1 0 NA NA
ctDNA.pCR=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1.0 0.000 1.000 1.000
24 1 3 0.5 0.204 0.111 0.804
ctDNA.pCR=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.000 0.00 1.0000 1.000
24 2 7 0.225 0.14 0.0342 0.517circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.pCR, data = circ_data)
n= 39, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.pCRTRG4/5 ctDNA (-) 2.7343 15.3983 0.9416 2.904 0.003685 **
ctDNA.pCRTRG4/5 ctDNA (+) 2.8771 17.7633 0.8035 3.581 0.000343 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.pCRTRG4/5 ctDNA (-) 15.40 0.06494 2.432 97.49
ctDNA.pCRTRG4/5 ctDNA (+) 17.76 0.05630 3.678 85.80
Concordance= 0.811 (se = 0.04 )
Likelihood ratio test= 19.74 on 2 df, p=5e-05
Wald test = 13.12 on 2 df, p=0.001
Score (logrank) test = 22.59 on 2 df, p=1e-05#DFS by ctDNA at the MRD Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 40 14 NA 23.32 NA
ctDNA.MRD=POSITIVE 7 7 3.57 3.21 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA MRD window | All pts", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 40 0 1.000 0.0000 1.000 1.000
24 18 13 0.628 0.0836 0.442 0.766
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 47, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.5604 12.9415 0.5704 4.489 7.17e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 12.94 0.07727 4.231 39.59
Concordance= 0.666 (se = 0.047 )
Likelihood ratio test= 17.26 on 1 df, p=3e-05
Wald test = 20.15 on 1 df, p=7e-06
Score (logrank) test = 31.36 on 1 df, p=2e-08cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 12.94 (4.23-39.59); p = 0"#OS by ctDNA at the MRD Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 40 6 NA NA NA
ctDNA.MRD=POSITIVE 10 8 8.59 5.31 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 40 0 1.000 0.0000 1.000 1.000
24 23 5 0.849 0.0632 0.671 0.935
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 50, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.6769 14.5406 0.5942 4.505 6.64e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 14.54 0.06877 4.537 46.6
Concordance= 0.771 (se = 0.055 )
Likelihood ratio test= 19.56 on 1 df, p=1e-05
Wald test = 20.29 on 1 df, p=7e-06
Score (logrank) test = 33.05 on 1 df, p=9e-09cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 14.54 (4.54-46.6); p = 0"#RFS by ctDNA at the MRD Window & ypT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 31 8 NA NA NA
ctDNA.Stage.II.TNM=2 4 4 5.10 3.21 NA
ctDNA.Stage.II.TNM=3 9 6 19.34 11.55 NA
ctDNA.Stage.II.TNM=4 3 3 3.51 1.70 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & ypTN", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.000 1.000 1.000
24 15 8 0.698 0.091 0.481 0.838
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 4 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.0 0.000 1.0000 1.000
24 3 5 0.4 0.174 0.0981 0.697
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 47, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 2.6818 14.6110 0.6876 3.900 9.62e-05 ***
ctDNA.Stage.II.TNMctDNA(-) & T4 1.0893 2.9722 0.5407 2.015 0.0439 *
ctDNA.Stage.II.TNMctDNA(+) & T4 3.4934 32.8977 0.8353 4.182 2.89e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 14.611 0.06844 3.796 56.233
ctDNA.Stage.II.TNMctDNA(-) & T4 2.972 0.33645 1.030 8.576
ctDNA.Stage.II.TNMctDNA(+) & T4 32.898 0.03040 6.399 169.126
Concordance= 0.731 (se = 0.051 )
Likelihood ratio test= 21.92 on 3 df, p=7e-05
Wald test = 22.7 on 3 df, p=5e-05
Score (logrank) test = 37.96 on 3 df, p=3e-08#Repeat analysis to compare ctDNA MRD (-) vs (+) in T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 31 8 NA NA NA
ctDNA.Stage.II.TNM=2 4 4 5.10 3.21 NA
ctDNA.Stage.II.TNM=3 9 6 19.34 11.55 NA
ctDNA.Stage.II.TNM=4 3 3 3.51 1.70 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & ypTN", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.000 1.000 1.000
24 15 8 0.698 0.091 0.481 0.838
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 4 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.0 0.000 1.0000 1.000
24 3 5 0.4 0.174 0.0981 0.697
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 47, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNM4 0.81163 2.25158 0.80842 1.004 0.3154
ctDNA.Stage.II.TNM1 -2.68177 0.06844 0.68763 -3.900 9.62e-05 ***
ctDNA.Stage.II.TNM3 -1.59246 0.20342 0.71596 -2.224 0.0261 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNM4 2.25158 0.4441 0.46170 10.9804
ctDNA.Stage.II.TNM1 0.06844 14.6110 0.01778 0.2634
ctDNA.Stage.II.TNM3 0.20342 4.9158 0.05000 0.8276
Concordance= 0.731 (se = 0.051 )
Likelihood ratio test= 21.92 on 3 df, p=7e-05
Wald test = 22.7 on 3 df, p=5e-05
Score (logrank) test = 37.96 on 3 df, p=3e-08#OS by ctDNA at the MRD Window & ypT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 31 3 NA NA NA
ctDNA.Stage.II.TNM=2 5 3 21.48 8.66 NA
ctDNA.Stage.II.TNM=3 9 3 49.70 49.70 NA
ctDNA.Stage.II.TNM=4 5 5 5.31 0.58 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypTN", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.0000 1.000 1.000
24 18 3 0.875 0.0681 0.658 0.959
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.000 0.000 1.000 1.000
24 5 2 0.762 0.148 0.332 0.935
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 50, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 2.5315 12.5725 0.8498 2.979 0.00289 **
ctDNA.Stage.II.TNMctDNA(-) & T4 1.2740 3.5751 0.8169 1.560 0.11886
ctDNA.Stage.II.TNMctDNA(+) & T4 4.5082 90.7580 0.9252 4.873 1.1e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 12.573 0.07954 2.377 66.49
ctDNA.Stage.II.TNMctDNA(-) & T4 3.575 0.27971 0.721 17.73
ctDNA.Stage.II.TNMctDNA(+) & T4 90.758 0.01102 14.804 556.40
Concordance= 0.829 (se = 0.056 )
Likelihood ratio test= 27.86 on 3 df, p=4e-06
Wald test = 24.63 on 3 df, p=2e-05
Score (logrank) test = 59.15 on 3 df, p=9e-13#Repeat analysis to compare ctDNA post-NAT (-) vs (+) in T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 31 3 NA NA NA
ctDNA.Stage.II.TNM=2 5 3 21.48 8.66 NA
ctDNA.Stage.II.TNM=3 9 3 49.70 49.70 NA
ctDNA.Stage.II.TNM=4 5 5 5.31 0.58 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypTN", ylab= "Overall Survival Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.0000 1.000 1.000
24 18 3 0.875 0.0681 0.658 0.959
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.000 0.000 1.000 1.000
24 5 2 0.762 0.148 0.332 0.935
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 50, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNM4 1.97669 7.21877 0.84482 2.340 0.01930 *
ctDNA.Stage.II.TNM1 -2.53151 0.07954 0.84981 -2.979 0.00289 **
ctDNA.Stage.II.TNM3 -1.25752 0.28436 0.84413 -1.490 0.13629
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNM4 7.21877 0.1385 1.37833 37.8071
ctDNA.Stage.II.TNM1 0.07954 12.5725 0.01504 0.4207
ctDNA.Stage.II.TNM3 0.28436 3.5167 0.05437 1.4873
Concordance= 0.829 (se = 0.056 )
Likelihood ratio test= 27.86 on 3 df, p=4e-06
Wald test = 24.63 on 3 df, p=2e-05
Score (logrank) test = 59.15 on 3 df, p=9e-13#RFS by ctDNA at the MRD Window & ypN
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & ypNStatus == "N0" ~ 1,
ctDNA.MRD == "POSITIVE" & ypNStatus == "N0" ~ 2,
ctDNA.MRD == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
ctDNA.MRD == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 24 5 NA NA NA
ctDNA.Stage.II.TNM=2 1 1 1.70 NA NA
ctDNA.Stage.II.TNM=3 16 9 15.20 10.34 NA
ctDNA.Stage.II.TNM=4 6 6 3.97 3.51 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & ypN", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.000 1.00
24 17 4 0.823 0.0807 0.593 0.93
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 1 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 16 0 1.000 0.000 1.00000 1.000
24 1 9 0.148 0.132 0.00842 0.466
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 47, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & ypN0 5.9532 384.9884 1.5428 3.859 0.000114 ***
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 2.0297 7.6117 0.6062 3.348 0.000814 ***
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 3.6876 39.9503 0.7639 4.827 1.38e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & ypN0 384.988 0.002597 18.715 7919.53
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 7.612 0.131377 2.320 24.97
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 39.950 0.025031 8.939 178.56
Concordance= 0.792 (se = 0.048 )
Likelihood ratio test= 31.15 on 3 df, p=8e-07
Wald test = 27.95 on 3 df, p=4e-06
Score (logrank) test = 54.44 on 3 df, p=9e-12#OS by ctDNA at the MRD Window & ypN
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & ypNStatus == "N0" ~ 1,
ctDNA.MRD == "POSITIVE" & ypNStatus == "N0" ~ 2,
ctDNA.MRD == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
ctDNA.MRD == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 24 2 NA NA NA
ctDNA.Stage.II.TNM=2 2 2 4.46 0.38 NA
ctDNA.Stage.II.TNM=3 16 4 NA 21.25 NA
ctDNA.Stage.II.TNM=4 8 6 9.46 5.54 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypN", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.000 1.000
24 20 1 0.957 0.0425 0.729 0.994
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 2 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 16 0 1.000 0.00 1.000 1.000
24 3 4 0.565 0.19 0.165 0.835
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 50, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & ypN0 5.0035 148.9381 1.1735 4.264 2.01e-05 ***
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 1.8810 6.5597 0.9016 2.086 0.037 *
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 3.5092 33.4225 0.8993 3.902 9.53e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & ypN0 148.94 0.006714 14.932 1485.5
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 6.56 0.152445 1.120 38.4
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 33.42 0.029920 5.736 194.8
Concordance= 0.86 (se = 0.04 )
Likelihood ratio test= 26.4 on 3 df, p=8e-06
Wald test = 23 on 3 df, p=4e-05
Score (logrank) test = 46.69 on 3 df, p=4e-10#DFS by ctDNA at the MRD Window & TRG combination
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.pCR <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.MRD == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
ctDNA.MRD == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
ctDNA.MRD == "POSITIVE" & TRG == "TRG4/5" ~ "3"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.pCR, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.pCR=1 23 6 NA 39.09 NA
ctDNA.pCR=2 12 6 15.60 11.55 NA
ctDNA.pCR=3 3 3 4.36 3.21 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA MRD/TRG", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.pCR, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.pCR=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 23 0 1.000 0.0000 1.000 1.000
24 12 5 0.749 0.0993 0.492 0.889
ctDNA.pCR=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 12 0 1.000 0.000 1.000 1.000
24 4 6 0.417 0.157 0.131 0.686
ctDNA.pCR=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1 0 1 1 circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.pCR, data = circ_data)
n= 38, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.pCRTRG4/5 ctDNA (-) 0.9323 2.5404 0.5794 1.609 0.107610
ctDNA.pCRTRG4/5 ctDNA (+) 3.0190 20.4704 0.8850 3.411 0.000647 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.pCRTRG4/5 ctDNA (-) 2.54 0.39364 0.816 7.909
ctDNA.pCRTRG4/5 ctDNA (+) 20.47 0.04885 3.612 116.004
Concordance= 0.7 (se = 0.064 )
Likelihood ratio test= 10.66 on 2 df, p=0.005
Wald test = 11.83 on 2 df, p=0.003
Score (logrank) test = 19.3 on 2 df, p=6e-05#OS by ctDNA at the MRD Window & TRG combination
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_data$ctDNA.pCR <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.MRD == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
ctDNA.MRD == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
ctDNA.MRD == "POSITIVE" & TRG == "TRG4/5" ~ "3"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.pCR, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.pCR=1 23 3 NA 49.70 NA
ctDNA.pCR=2 12 3 NA 16.09 NA
ctDNA.pCR=3 6 6 5.42 0.58 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA MRD/TRG", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.pCR, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.pCR=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 23 0 1.000 0.0000 1.000 1.000
24 13 2 0.886 0.0776 0.607 0.971
ctDNA.pCR=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 12 0 1.0 0.000 1.000 1.000
24 6 3 0.7 0.145 0.329 0.892
ctDNA.pCR=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1 0 1 1 circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.pCR, data = circ_data)
n= 41, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.pCRTRG4/5 ctDNA (-) 0.6450 1.9061 0.8202 0.786 0.432
ctDNA.pCRTRG4/5 ctDNA (+) 3.1621 23.6201 0.7401 4.273 1.93e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.pCRTRG4/5 ctDNA (-) 1.906 0.52464 0.3819 9.513
ctDNA.pCRTRG4/5 ctDNA (+) 23.620 0.04234 5.5374 100.753
Concordance= 0.812 (se = 0.065 )
Likelihood ratio test= 18.73 on 2 df, p=9e-05
Wald test = 21.36 on 2 df, p=2e-05
Score (logrank) test = 38.75 on 2 df, p=4e-09#DFS by ctDNA at the MRD Window & ypTN Characteristics
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE,]
circ_data$DFS.months <- circ_data$DFS.months - 3
circ_data <- circ_data[circ_data$DFS.months >= 0,]
circ_data$ctDNA.pCR <- NA
# Define new categories
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage=="T1-T3" & ypNStatus == "N0" ~ "1",
ctDNA.MRD == "POSITIVE" | pT.Stage == "T4" | ypNStatus == "N1-N3" ~ "2"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.pCR, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.pCR=1 19 2 NA NA NA
ctDNA.pCR=2 30 20 11.9 8.17 39.1surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data, conf.int = 0.95, conf.type = "log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE,
break.time.by = 12, palette = c("blue", "red"),
title = "DFS - ctDNA MRD/Clinical Characteristics",
ylab = "Disease-Free Survival", xlab = "Time from Landmark Time point (Months)",
legend.labs = c("MRD neg, T1-T3, N0", "MRD pos or T4 or N1-N3"),
legend.title = "")
summary(KM_curve, times = c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.pCR, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.pCR=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 19 0 1.000 0.000 1.000 1.000
24 14 2 0.881 0.079 0.602 0.969
ctDNA.pCR=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 30 0 1.000 0.0000 1.000 1.000
24 5 19 0.268 0.0954 0.107 0.461circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2"), labels=c("MRD neg, T1-T3, N0","MRD pos or T4 or N1-N3"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.pCR, data = circ_data)
n= 49, number of events= 22
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.pCRMRD pos or T4 or N1-N3 2.5366 12.6367 0.7503 3.381 0.000723 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.pCRMRD pos or T4 or N1-N3 12.64 0.07913 2.904 54.99
Concordance= 0.723 (se = 0.036 )
Likelihood ratio test= 20.4 on 1 df, p=6e-06
Wald test = 11.43 on 1 df, p=7e-04
Score (logrank) test = 18 on 1 df, p=2e-05cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 12.64 (2.9-54.99); p = 0.001"#OS by ctDNA at the MRD Window & ypTN Characteristics
# Clear workspace and set working directory
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE,]
circ_data$FU.months <- circ_data$FU.months - 3
circ_data <- circ_data[circ_data$FU.months >= 0,]
circ_data$ctDNA.pCR <- NA
# Define new categories
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage=="T1-T3" & ypNStatus == "N0" ~ "1",
ctDNA.MRD == "POSITIVE" | pT.Stage == "T4" | ypNStatus == "N1-N3" ~ "2"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.pCR, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.pCR=1 19 0 NA NA NA
ctDNA.pCR=2 34 16 21.5 16.1 NAsurv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data, conf.int = 0.95, conf.type = "log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE,
break.time.by = 12, palette = c("blue", "red"),
title = "OS - ctDNA MRD/Clinical Characteristics",
ylab = "Overall Survival", xlab = "Time from Landmark Time point (Months)",
legend.labs = c("MRD neg, T1-T3, N0", "MRD pos or T4 or N1-N3"),
legend.title = "")
summary(KM_curve, times = c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.pCR, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.pCR=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 19 0 1 0 1 1
24 16 0 1 0 NA NA
ctDNA.pCR=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 34 0 1.000 0.000 1.000 1.000
24 8 15 0.446 0.105 0.239 0.634circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2"), labels=c("MRD neg, T1-T3, N0","MRD pos or T4 or N1-N3"))
cox_fit <- coxphf(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.pCR, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.pCRMRD pos or T4 or N1-N3 3.493535 1.495302 32.90204 4.363659 4215.243 18.01942 2.186631e-05
Likelihood ratio test=18.01942 on 1 df, p=2.186631e-05, n=53
Wald test = 5.458488 on 1 df, p = 0.01947349
Covariance-Matrix:
ctDNA.pCRMRD pos or T4 or N1-N3
ctDNA.pCRMRD pos or T4 or N1-N3 2.235927#Multivariate cox regression for RFS - ctDNA MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + Age.Group + pT.Stage + ypNStatus + TRG, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for RFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#Multivariate cox regression for OS - ctDNA MRD Window v1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + Gender + Age.Group + ypNStatus + TRG, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#Multivariate cox regression for OS - ctDNA MRD Window v2
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + Age.Group + pT.Stage + ypNStatus + TRG, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#DFS by ctDNA at the Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.surveillance=NEGATIVE 33 10 NA 39.09 NA
ctDNA.surveillance=POSITIVE 4 4 6.27 3.21 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA Surveillance window | All pts", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 33 0 1.000 0.0000 1.00 1.00
24 16 9 0.679 0.0896 0.47 0.82
ctDNA.surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 4 0 1 0 1 1 circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.surveillance, data = circ_data)
n= 37, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.surveillancePOSITIVE 2.7504 15.6482 0.7292 3.772 0.000162 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.surveillancePOSITIVE 15.65 0.06391 3.748 65.34
Concordance= 0.657 (se = 0.06 )
Likelihood ratio test= 11.52 on 1 df, p=7e-04
Wald test = 14.23 on 1 df, p=2e-04
Score (logrank) test = 24.6 on 1 df, p=7e-07cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 15.65 (3.75-65.34); p = 0"#OS by ctDNA at the Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.surveillance!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.surveillance=NEGATIVE 33 5 NA NA NA
ctDNA.surveillance=POSITIVE 5 3 8.66 5.54 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 33 0 1.000 0.0000 1.000 1.000
24 20 3 0.886 0.0626 0.684 0.962
ctDNA.surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1 circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.surveillance, data = circ_data)
n= 38, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.surveillancePOSITIVE 2.7378 15.4530 0.9239 2.963 0.00304 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.surveillancePOSITIVE 15.45 0.06471 2.527 94.5
Concordance= 0.727 (se = 0.088 )
Likelihood ratio test= 8.06 on 1 df, p=0.005
Wald test = 8.78 on 1 df, p=0.003
Score (logrank) test = 15.41 on 1 df, p=9e-05cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 15.45 (2.53-94.5); p = 0.003"---
title: "AGEO PLAGAST_Clinical Final analysis 06122024"
output: html_notebook
---
library(swimplot)
library(coxphf)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival) 
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(ComplexHeatmap)
library(tidyverse)
library(readxl)
library(survival)
library(janitor)
library(openxlsx)
library(writexl)
library(rms)
library(pROC)
library(DT)

#ctDNA Detection rate by Stage and Window
```{r}
#Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#C2D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.C2D1 %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C2D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C2D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C2D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#post-NAC Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postNAC %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postNAC == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postNAC, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postNAC == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#On-treatment
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.onACT <- factor(circ_data$ctDNA.onACT, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.onACT %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.onACT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.onACT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.onACT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Post-ACT Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postACT <- factor(circ_data$ctDNA.postACT, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postACT %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postACT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postACT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postACT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Post-relapse Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postRelapse <- factor(circ_data$ctDNA.postRelapse, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postRelapse %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postRelapse == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postRelapse, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postRelapse == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```




#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    NAC,
    NAC.Regimen,
    TRG.Mandard,
    TNM,
    Stage,
    Grade,
    Lauren.Class,
    Margins,
    ACT,
    ACT.Regimen,
    DFS.Event,
    OS.Event,
    FU.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Stomach", "G/J", "Oesophagus")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Therapy", "Upfront Surgery")),
    NAC.Regimen = factor(NAC.Regimen),
    TRG.Mandard = factor(TRG.Mandard, levels = c("TRG1","TRG2", "TRG3", "TRG4", "TRG5")),
    TNM = factor(TNM, levels = c("T0-TisN0M0","T1-T2N0", "T2-T3N0-N1", "T2N1-N2", "T3N2-N3", "T4N0-N1", "T4N2-N3")),
    Stage = factor(Stage, levels = c("0","I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3")),
    Lauren.Class = factor(Lauren.Class),
    Margins = factor(Margins, levels = c("R0", "R1")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Treatment", "Observation")),
    ACT.Regimen = factor(ACT.Regimen),
    DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
    FU.months = as.numeric(FU.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```


#Heatmap with Clinical & Genomics Factors
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  Stage = circ_data$Stage,
  Gender = circ_data$Gender,
  PrimSite = circ_data$PrimSite,
  NAC = circ_data$NAC,
  ACT = circ_data$ACT,
  ctDNA.Base = circ_data$ctDNA.Base,
  ctDNA.C2D1 = circ_data$ctDNA.C2D1,
  ctDNA.postNAC = circ_data$ctDNA.postNAC,
  ctDNA.MRD = circ_data$ctDNA.MRD,
  ctDNA.surveillance = circ_data$ctDNA.surveillance,
  DFS.Event = circ_data$DFS.Event,
  OS.Event = circ_data$OS.Event,
  
  col = list(Stage = c("0" = "seagreen1", "I" = "seagreen1", "II" = "orange", "III" = "purple"),
    Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
    PrimSite = c("Stomach" = "brown", "G/J" = "darkgreen", "Oesophagus" = "orange4"),
    NAC = c("FALSE" = "cornflowerblue", "TRUE" ="darkmagenta"),
    ACT = c("TRUE" = "brown4", "FALSE" ="khaki"),
    ctDNA.Base = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.C2D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.postNAC = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    DFS.Event = c("TRUE" = "red3", "FALSE" ="blue"),
    OS.Event = c("TRUE" = "black", "FALSE" ="gray")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()
```


#Overview Plot
```{r}
setwd("~/Downloads") 
clinstage<- read.csv("PLAGAST_OP.csv")
clinstage_df<- as.data.frame(clinstage)

#Display the swimmer plot with the label box
oplot<-swimmer_plot(df=clinstage_df,
                    id='PatientName',
                    end='fu.diff.months',
                    fill='gray',
                    width=.01,)
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(-12, 96, by = 6))
oplot <- oplot + labs(x ="Patients" , y="Months from Surgery")
oplot


##plot events
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
                                    id='PatientName',
                                    time='date.diff.months',
                                    name_shape ='Event_type',
                                    name_col = 'Event',
                                    size=3.5,fill='black',
                                    #col='darkgreen'
)
oplot_ev1

#Shape customization to Event_type

oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",values=c(1,16,6,18,4),breaks=c('ctDNA_neg','ctDNA_pos','Imaging','Surgery','Death'))

oplot_ev1.1

#plot treatment

oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2  


#colour customization
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "purple","black","black", "lightblue", "green", "red", "blue","orange"))
oplot_ev2.2
```


#RFS by ctDNA at Baseline - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Base, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA Baseline | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at Baseline - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Base, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Baseline | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#RFS by ctDNA levels at Baseline based on median
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

# Calculate the quartiles of p_6mo_MTM
circ_data$ctDNA.Base.MTM <- as.numeric(as.character(circ_data$ctDNA.Base.MTM))
median_value <- median(circ_data$ctDNA.Base.MTM, na.rm = TRUE)
print(median_value)

# Create a new variable based on these quartiles

circ_data$ctDNA.6mMTM.Q <- NA
circ_data <- circ_data %>%
  mutate(ctDNA.6mMTM.Q = case_when(
    ctDNA.Base.MTM < 0.385 ~ 1,
    ctDNA.Base.MTM >= 0.385 ~ 2
  ))

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.6mMTM.Q, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA MTM/mL groups at Baseline", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.385", "MTM/mL≥0.385"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.385", "MTM/mL≥0.385"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by ctDNA levels at Baseline based on median
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

# Calculate the quartiles of p_6mo_MTM
circ_data$ctDNA.Base.MTM <- as.numeric(as.character(circ_data$ctDNA.Base.MTM))
median_value <- median(circ_data$ctDNA.Base.MTM, na.rm = TRUE)
print(median_value)

# Create a new variable based on these quartiles

circ_data$ctDNA.6mMTM.Q <- NA
circ_data <- circ_data %>%
  mutate(ctDNA.6mMTM.Q = case_when(
    ctDNA.Base.MTM < 0.385 ~ 1,
    ctDNA.Base.MTM >= 0.385 ~ 2
  ))

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.6mMTM.Q, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MTM/mL groups at Baseline", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.385", "MTM/mL≥0.385"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.385", "MTM/mL≥0.385"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#RFS by ctDNA levels at Baseline based on AUC optimal MTM/ml level
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

#DFS.Event
circ_data <- circ_data[complete.cases(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM),]
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
ROC <- roc(DFS.Event ~  ctDNA.Base.MTM, data = circ_data, ci = TRUE)
p<-ggroc(ROC,
         aes = c("linetype"), color = "blue",  size = 1,
         legacy.axes = TRUE) +
  geom_abline(color = "dark grey", size = 0.5) +
  theme_classic()+
  ylab("Sensitivity") + theme(axis.title.x = element_text(color="black", size=14), axis.title.y = element_text(color="black", size=14),axis.text.x = element_text(colour = "black", size=14),axis.text.y = element_text(colour = "black",size=14),legend.title  = element_blank(),legend.text = element_text(size=14))
p

#AUC
AUC <- auc(ROC)
print(AUC)
AUC_conf <- ci.auc(ROC)
print(AUC_conf)
res.roc <- roc(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM)
plot.roc(res.roc, print.auc = TRUE, print.thres = "best")

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

# Create a new variable based on these quartiles

circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.6mMTM.Q = case_when(
    ctDNA.Base.MTM < 0.845 ~ 1,
    ctDNA.Base.MTM >= 0.845 ~ 2
  ))

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.6mMTM.Q, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA MTM/mL groups at Baseline", ylab= "Disease-Free Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.845", "MTM/mL≥0.845"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.845", "MTM/mL≥0.845"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by ctDNA levels at Baseline based on AUC optimal MTM/mL level
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

#OS.Event
circ_data <- circ_data[complete.cases(circ_data$OS.Event, circ_data$ctDNA.Base.MTM),]
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
ROC <- roc(OS.Event ~  ctDNA.Base.MTM, data = circ_data, ci = TRUE)
p<-ggroc(ROC,
         aes = c("linetype"), color = "blue",  size = 1,
         legacy.axes = TRUE) +
  geom_abline(color = "dark grey", size = 0.5) +
  theme_classic()+
  ylab("Sensitivity") + theme(axis.title.x = element_text(color="black", size=14), axis.title.y = element_text(color="black", size=14),axis.text.x = element_text(colour = "black", size=14),axis.text.y = element_text(colour = "black",size=14),legend.title  = element_blank(),legend.text = element_text(size=14))
p

#AUC
AUC <- auc(ROC)
print(AUC)
AUC_conf <- ci.auc(ROC)
print(AUC_conf)
res.roc <- roc(circ_data$OS.Event, circ_data$ctDNA.Base.MTM)
plot.roc(res.roc, print.auc = TRUE, print.thres = "best")

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

# Create a new variable based on these quartiles

circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.6mMTM.Q = case_when(
    ctDNA.Base.MTM < 11.305 ~ 1,
    ctDNA.Base.MTM >= 11.305 ~ 2
  ))

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.6mMTM.Q, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MTM/mL groups at Baseline", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<11.305", "MTM/mL≥11.305"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<11.305", "MTM/mL≥11.305"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by ctDNA levels at Baseline based on AUC optimal MTM/mL level from RFS model
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

# Create a new variable based on these quartiles

circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.6mMTM.Q = case_when(
    ctDNA.Base.MTM < 0.845 ~ 1,
    ctDNA.Base.MTM >= 0.845 ~ 2
  ))

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.6mMTM.Q, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MTM/mL groups at Baseline", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.845", "MTM/mL≥0.845"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.845", "MTM/mL≥0.845"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#RFS by ctDNA on-NAT - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.C2D1!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.C2D1, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA status on-NAT | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA on-NAT - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.C2D1!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.C2D1, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA status on-NAT | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#DFS by ctDNA Clearance during NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.C2D1.Clearance <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C2D1.Clearance = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" ~ "TRUE",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" ~ "FALSE",
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.C2D1.Clearance),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.C2D1.Clearance, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("red","blue"), title="RFS - ctDNA clearance C2D1", ylab= "Recurrence-Free Survival", xlab="Months from Surgery", legend.labs=c("No Clearance", "Clearance"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.C2D1.Clearance <- factor(circ_data$ctDNA.C2D1.Clearance, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1.Clearance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#OS by ctDNA Clearance during NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.C2D1.Clearance <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C2D1.Clearance = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" ~ "TRUE",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" ~ "FALSE",
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.C2D1.Clearance),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.C2D1.Clearance, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("red","blue"), title="OS - ctDNA clearance C2D1", ylab= "Overall Survival", xlab="Months from Surgery", legend.labs=c("No Clearance", "Clearance"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.C2D1.Clearance <- factor(circ_data$ctDNA.C2D1.Clearance, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1.Clearance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#RFS by ctDNA post-NAT - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.postNAC, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postNAC, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA status post-NAT | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postNAC, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA post-NAT - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.postNAC, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postNAC, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA status post-NAT | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postNAC, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#Multivariate cox regression for RFS - ctDNA post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.postNAC + PrimSite + Gender + Age.Group + ACT, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for RFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#Multivariate cox regression for OS - ctDNA post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.postNAC + PrimSite + Gender + Age.Group + ACT, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#RFS by ctDNA post-NAT & ypT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.postNAC == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.postNAC == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA post-NAT & ypTN", ylab= "Disease-Free Survival", xlab="Time from Surgery (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)

#Repeat analysis to compare ctDNA post-NAT (+) T1-T3 vs T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.postNAC == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.postNAC == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#OS by ctDNA post-NAT & ypT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.postNAC == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.postNAC == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA post-NAT & ypTN", ylab= "Overall Survival", xlab="Time from Surgery (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)

#Repeat analysis to compare ctDNA post-NAT (+) T1-T3 vs T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.postNAC == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.postNAC == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.postNAC == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#RFS by ctDNA post-NAT & ypN
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N0" ~ 1,
    ctDNA.postNAC == "POSITIVE" & ypNStatus == "N0" ~ 2,
    ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
    ctDNA.postNAC == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA post-NAT & ypN", ylab= "Disease-Free Survival", xlab="Time from Surgery (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)

#Repeat analysis to compare ctDNA post-NAT (-) vs (+) in ypN+
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N0" ~ 1,
    ctDNA.postNAC == "POSITIVE" & ypNStatus == "N0" ~ 2,
    ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
    ctDNA.postNAC == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#OS by ctDNA post-NAT & ypN
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N0" ~ 1,
    ctDNA.postNAC == "POSITIVE" & ypNStatus == "N0" ~ 2,
    ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
    ctDNA.postNAC == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA post-NAT & ypN", ylab= "Overall Survival", xlab="Time from Surgery (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)

#Repeat analysis to compare ctDNA post-NAT (+) ypN0 vs ypN+
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N0" ~ 1,
    ctDNA.postNAC == "POSITIVE" & ypNStatus == "N0" ~ 2,
    ctDNA.postNAC == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
    ctDNA.postNAC == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#DFS by ctDNA Clearance post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.C2D1.Clearance <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C2D1.Clearance = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "TRUE",
    ctDNA.Base == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "FALSE",
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.C2D1.Clearance),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.C2D1.Clearance, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("red","blue"), title="RFS - ctDNA clearance post-NAT", ylab= "Recurrence-Free Survival", xlab="Months from Surgery", legend.labs=c("No Clearance", "Clearance"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.C2D1.Clearance <- factor(circ_data$ctDNA.C2D1.Clearance, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1.Clearance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#OS by ctDNA Clearance post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.C2D1.Clearance <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C2D1.Clearance = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "TRUE",
    ctDNA.Base == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "FALSE",
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.C2D1.Clearance),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.C2D1.Clearance, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("red","blue"), title="OS - ctDNA clearance post-NAT", ylab= "Overall Survival", xlab="Months from Surgery", legend.labs=c("No Clearance", "Clearance"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.C2D1.Clearance <- factor(circ_data$ctDNA.C2D1.Clearance, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1.Clearance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Association of ctDNA Dynamics post-NAT and Response
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

#Vertical Fisher plot for ctDNA clearance post-NAT and Rec Status
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-", "ctDNA +/+/-", "ctDNA +/+/+"))
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$TRG)
fisher_exact_test <- fisher.test(contingency_table)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2

# Swapping x and y in ggplot function to make bar plot vertical
ggplot(table_df, aes(y = Var1, x = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(x = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA Dymamics post-NAT", y = "ctDNA", x = "Patients (%)", fill = "Path Response") +
  scale_x_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("TRG1/2/3" = "lightblue3", "TRG4/5" = "red")) + # define custom colors
  theme(axis.text.y = element_text(angle = 0, hjust = 1.5, size = 14), # increase y-axis text size
        axis.text.x = element_text(size = 14, color = "black"), # increase x-axis text size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculating p-value with Fisher exact test for the ctDNA +/+/+ vs ctDNA +/+/-
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

#Vertical Fisher plot for ctDNA clearance post-NAT and Rec Status
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="ctDNA +/-/-",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/+/-", "ctDNA +/+/+"))
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$TRG)
fisher_exact_test <- fisher.test(contingency_table)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
print(fisher_exact_test)
print(contingency_table)
```


#DFS by ctDNA Dynamics post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA Dynamics post-NAT", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)
```


#OS by ctDNA Dynamics post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA Dynamics post-NAT", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
cox_fit <- coxphf(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)
```


#Levels of Baseline MTM/mL in ctDNA Dynamics post-NAT categories
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

# Transform ctDNA.Base.MTM with log10
circ_data <- subset(circ_data, !is.na(ctDNA.Dynamics))
circ_data$ctDNA.Base.MTM <- as.numeric(as.character(circ_data$ctDNA.Base.MTM))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
median_Base_MTM <- aggregate(ctDNA.Base.MTM ~ ctDNA.Dynamics, data = circ_data, FUN = median)
print(median_Base_MTM)

# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=ctDNA.Dynamics, y=ctDNA.Base.MTM, fill=ctDNA.Dynamics)) +
  geom_violin(trim=FALSE) +
  scale_fill_manual(values=c("ctDNA +/-/-"="lightblue", "ctDNA +/+/-"="lightgreen", "ctDNA +/+/+"="salmon")) +
  geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
  scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
  labs(title="Baseline MTM/mL | Dynamics post-NAT", x="Dynamics post-NAT", y="Baseline MTM/mL") +
  theme_minimal() +
  theme(legend.position="none")
m3_1v2 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
                      data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/-/-", "ctDNA +/+/-"), ],
                      na.rm = TRUE)
print(m3_1v2)
m3_1v3 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
                      data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/-/-", "ctDNA +/+/+"), ],
                      na.rm = TRUE)
print(m3_1v3)
m3_2v3 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
                      data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/+/-", "ctDNA +/+/+"), ],
                      na.rm = TRUE)
print(m3_2v3)
```

#DFS by ctDNA post-NAT & TRG combination
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.pCR <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.postNAC == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
    ctDNA.postNAC == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
    ctDNA.postNAC == "POSITIVE" & TRG == "TRG4/5" ~ "3"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA post-NAT/TRG", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
```


#OS by ctDNA post-NAT & TRG combination
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.pCR <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.postNAC == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
    ctDNA.postNAC == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
    ctDNA.postNAC == "POSITIVE" & TRG == "TRG4/5" ~ "3"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA post-NAT/TRG", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
```


#DFS by ctDNA at the MRD Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA MRD window | All pts", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at the MRD Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```





#RFS by ctDNA at the MRD Window & ypT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & ypTN", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)

#Repeat analysis to compare ctDNA MRD (-) vs (+) in T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & ypTN", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#OS by ctDNA at the MRD Window & ypT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypTN", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)

#Repeat analysis to compare ctDNA post-NAT (-) vs (+) in T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypTN", ylab= "Overall Survival Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#RFS by ctDNA at the MRD Window & ypN
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & ypNStatus == "N0" ~ 1,
    ctDNA.MRD == "POSITIVE" & ypNStatus == "N0" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
    ctDNA.MRD == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & ypN", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#OS by ctDNA at the MRD Window & ypN
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & ypNStatus == "N0" ~ 1,
    ctDNA.MRD == "POSITIVE" & ypNStatus == "N0" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
    ctDNA.MRD == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypN", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#DFS by ctDNA at the MRD Window & TRG combination
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.pCR <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.MRD == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
    ctDNA.MRD == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
    ctDNA.MRD == "POSITIVE" & TRG == "TRG4/5" ~ "3"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA MRD/TRG", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
```


#OS by ctDNA at the MRD Window & TRG combination
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]

circ_data$ctDNA.pCR <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.MRD == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
    ctDNA.MRD == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
    ctDNA.MRD == "POSITIVE" & TRG == "TRG4/5" ~ "3"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA MRD/TRG", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
```


#DFS by ctDNA at the MRD Window & ypTN Characteristics
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE,]
circ_data$DFS.months <- circ_data$DFS.months - 3
circ_data <- circ_data[circ_data$DFS.months >= 0,]
circ_data$ctDNA.pCR <- NA

# Define new categories
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage=="T1-T3" & ypNStatus == "N0" ~ "1",
    ctDNA.MRD == "POSITIVE" | pT.Stage == "T4" | ypNStatus == "N1-N3" ~ "2"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data, conf.int = 0.95, conf.type = "log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, 
           break.time.by = 12, palette = c("blue", "red"), 
           title = "DFS - ctDNA MRD/Clinical Characteristics", 
           ylab = "Disease-Free Survival", xlab = "Time from Landmark Time point (Months)", 
           legend.labs = c("MRD neg, T1-T3, N0", "MRD pos or T4 or N1-N3"), 
           legend.title = "")
summary(KM_curve, times = c(0, 24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2"), labels=c("MRD neg, T1-T3, N0","MRD pos or T4 or N1-N3"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by ctDNA at the MRD Window & ypTN Characteristics
```{r}
# Clear workspace and set working directory
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE,]
circ_data$FU.months <- circ_data$FU.months - 3
circ_data <- circ_data[circ_data$FU.months >= 0,]
circ_data$ctDNA.pCR <- NA

# Define new categories
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage=="T1-T3" & ypNStatus == "N0" ~ "1",
    ctDNA.MRD == "POSITIVE" | pT.Stage == "T4" | ypNStatus == "N1-N3" ~ "2"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data, conf.int = 0.95, conf.type = "log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, 
           break.time.by = 12, palette = c("blue", "red"), 
           title = "OS - ctDNA MRD/Clinical Characteristics", 
           ylab = "Overall Survival", xlab = "Time from Landmark Time point (Months)", 
           legend.labs = c("MRD neg, T1-T3, N0", "MRD pos or T4 or N1-N3"), 
           legend.title = "")
summary(KM_curve, times = c(0, 24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2"), labels=c("MRD neg, T1-T3, N0","MRD pos or T4 or N1-N3"))
cox_fit <- coxphf(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
```

#Multivariate cox regression for RFS - ctDNA MRD Window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + Age.Group + pT.Stage + ypNStatus + TRG, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for RFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#Multivariate cox regression for OS - ctDNA MRD Window v1
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + Gender + Age.Group + ypNStatus + TRG, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#Multivariate cox regression for OS - ctDNA MRD Window v2
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("G/J","Stomach"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + Age.Group + pT.Stage + ypNStatus + TRG, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```

#DFS by ctDNA at the Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.surveillance, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA Surveillance window | All pts", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at the Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.surveillance!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.surveillance, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




